Record leiden/louvain modularity in adata.uns

docs/release-notes/blank.md

@@ -2,6 +2,8 @@
 
 ```{rubric} Features
 ```
+* ``tl.leiden`` and ``tl.louvain`` now record the final modularity value in ``adata.uns[key_added]["modularity"]``
+(scalar for a single resolution, list for multiple resolutions) {pr}`648` {smaller}`J Pintar`
 
 ```{rubric} Performance
 ```
@@ -13,6 +15,9 @@
 
 ```{rubric} Misc
 ```
+* ``adata.uns[key_added]["params"]["resolution"]`` is now stored as a scalar ``float`` when a single resolution
+is passed to ``tl.leiden`` and ``tl.louvain`` to match behaviour in Scanpy, and as a ``list`` when multiple
+resolutions are passed. Previously it was always stored as a list. {pr}`648`. {smaller}`J Pintar`
 
 ```{rubric} Removals
 ```

src/rapids_singlecell/tools/_clustering.py

@@ -229,8 +229,9 @@ def leiden(
         resolutions = [resolution]
     else:
         resolutions = resolution
+    modularities = []
     for resolution in resolutions:
-        leiden_parts, _ = culeiden(
+        leiden_parts, modularity = culeiden(
             g,
             resolution=resolution,
             random_state=random_state,
@@ -241,6 +242,7 @@ def leiden(
             leiden_parts = leiden_parts.to_backend("pandas").compute()
         else:
             leiden_parts = leiden_parts.to_pandas()
+        modularities.append(modularity)
 
         # Format output
         groups = leiden_parts.sort_values("vertex")[["partition"]].to_numpy().ravel()
@@ -270,10 +272,13 @@ def leiden(
     # store information on the clustering parameters
     adata.uns[key_added] = {}
     adata.uns[key_added]["params"] = {
-        "resolution": resolutions,
+        "resolution": resolutions if len(resolutions) > 1 else resolutions[0],
         "random_state": random_state,
         "n_iterations": n_iterations,
     }
+    adata.uns[key_added]["modularity"] = (
+        modularities if len(modularities) > 1 else modularities[0]
+    )
     return adata if copy else None
 
 
@@ -383,8 +388,9 @@ def louvain(
         resolutions = [resolution]
     else:
         resolutions = resolution
+    modularities = []
     for resolution in resolutions:
-        louvain_parts, _ = culouvain(
+        louvain_parts, modularity = culouvain(
             g,
             resolution=resolution,
             max_level=n_iterations,
@@ -394,6 +400,7 @@ def louvain(
             louvain_parts = louvain_parts.to_backend("pandas").compute()
         else:
             louvain_parts = louvain_parts.to_pandas()
+        modularities.append(modularity)
 
         # Format output
         groups = louvain_parts.sort_values("vertex")[["partition"]].to_numpy().ravel()
@@ -422,10 +429,13 @@ def louvain(
         Comms.destroy()
     adata.uns[key_added] = {}
     adata.uns[key_added]["params"] = {
-        "resolution": resolutions,
+        "resolution": resolutions if len(resolutions) > 1 else resolutions[0],
         "n_iterations": n_iterations,
         "threshold": threshold,
     }
+    adata.uns[key_added]["modularity"] = (
+        modularities if len(modularities) > 1 else modularities[0]
+    )
     return adata if copy else None
 
 

tests/test_clustering.py

@@ -78,8 +78,13 @@ def test_clustering_resolution(adata_neighbors, clustering_function, resolution)
     if isinstance(resolution, list):
         for r in resolution:
             assert f"test_clustering_{r}" in adata.obs.columns
+        assert isinstance(adata.uns["test_clustering"]["modularity"], list)
+        assert len(adata.uns["test_clustering"]["modularity"]) == len(resolution)
+        assert adata.uns["test_clustering"]["params"]["resolution"] == resolution
     else:
         assert "test_clustering" in adata.obs.columns
+        assert isinstance(adata.uns["test_clustering"]["modularity"], float)
+        assert adata.uns["test_clustering"]["params"]["resolution"] == resolution
 
 
 def test_kmeans_basic(adata_neighbors):